AverageExplorer: Interactive Exploration and Alignment of Visual Data Collections



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This paper proposes an interactive framework that allows a user to rapidly explore and visualize a large image collection using the medium of average images. Average images have been gaining popularity as means of artistic expression and data visualization, but the creation of compelling examples is a surprisingly laborious and manual process. Our interactive, real-time system provides a way to summarize large amounts of visual data by weighted average(s) of an image collection, with the weights reflecting user-indicated importance. The aim is to capture not just the mean of the distribution, but a set of modes discovered via interactive exploration. We pose this exploration in terms of a user interactively “editing” the average image using various types of strokes, brushes and warps, similar to a normal image editor, with each user interaction providing a new constraint to update the average. New weighted averages can be spawned and edited either individually or jointly. Together, these tools allow the user to simultaneously perform two fundamental operations on visual data: user-guided clustering and user-guided alignment, within the same framework. We show that our system is useful for various computer vision and graphics applications

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SIGGRAPH paper. (pdf, 5MB)


(pptx + videos), 363MB


Jun-Yan Zhu, Yong Jae Lee, Alexei A. Efros. AverageExplorer: Interactive Exploration and Alignment of Visual Data Collections. ACM Transactions on Graphics (SIGGRAPH 2014). August 2014, vol. 33, No. 4.

Additional Results

Online Shopping

Our system could be useful to an online shopper when browsing products.(e.g. on AMAZON)


Average Cat Face for Different Cat Breeds

Trying moving your mouse over the image.
Click the image to see the keypoints/parts annotation.


Image Average "Refocusing"

We can focus on different parts of the "Bridge of Sighs". We can even generate the object-centric average in real-time.

Additional Materials


Code available soon.

Other work in Image Averaging


We thank Tinghui Zhou, Abhinav Shrivastava, Carl Doersch, and Shiry Ginosar for helpful insights and discussions, and the anonymous reviewers for valuable comments.


This research is supported in part by: